This model was given in [1]} as an example where Cut model approaches improve on Bayesian inference and analysed in [2]} as an example of \(\eta \) -SMI. Here we repeat this analysis for our new SMI variants. In this normal setup the three interpolations \(\eta \) -SMI, \(\delta \) -SMI and \(\gamma \) -SMI are all identical. We may take a fixed value of \(\delta \)  and recover the \(\eta \) -SMI and \(\gamma \) -SMI distributions by setting \(\eta =\sigma _y^2/(\sigma _y^2+\delta ^2)\)  and \(\gamma =\frac{\sigma _y^2}{\delta ^2+\sigma _y^2}+1\) .
